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Updated: May 29, 2025

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

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抗氧化剂二胺衍生物的合理设计使用定量结构-活性关系和量子力学计算

Ayokanmi Joseph Aremu1, Phiphob Naweephattana2, Ismail Dwi Putra3

  • 1Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand.

Journal of computational chemistry
|February 4, 2025
PubMed
概括

这项研究结合了机器学习和量子力学,为制品设计了具有增强抗氧化性能的新型二胺 (DPA) 衍生物. 开发的模型准确地预测了抗氧化剂活性,指导了具有改善原子转移能力的强效化合物的合成.

关键词:
在 DFT 方面,它是最重要的.抗氧化剂是一种抗氧化剂.基于机器学习的QSAR.合理的设计理性的设计.

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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